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DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training
Summary: DynaHB is a communication-avoiding distributed DGNN framework that combines vertex caching and load-aware vertex partitioning with novel hybrid batches (fusion of vertex- and snapshot-batches) to cut communication, GPU memory usage, and training time. An RL-based batch adjuster plus a pipelined batch generator with a batch reservoir amortizes hybrid-batch creation, yielding up to 93x (avg 8.06x) speedups over prior frameworks.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13551
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 5.3538607e-05
- Overall Rank
- 5,721 | 60.24%
- DOI
-
10.14778/3681954.3682008
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 271 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029565193 |
| 635 |
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding |
2021 |
SIGMOD |
0.00018825777 |
| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 3,028 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6833093e-05 |
| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5869574e-05 |
| 3,281 |
Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching |
2022 |
VLDB |
7.2812392e-05 |
| 3,715 |
Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank |
2023 |
VLDB |
6.8176818e-05 |
| 4,054 |
Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees |
2023 |
SIGMOD |
6.4910804e-05 |
| 5,016 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7512359e-05 |
| 5,356 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
5.5514335e-05 |
| 6,008 |
Compression of Uncertain Trajectories in Road Networks |
2020 |
VLDB |
5.2365238e-05 |
| 6,479 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
5.0405101e-05 |
| 7,568 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7044808e-05 |
| 9,453 |
TRACE: Real-time Compression of Streaming Trajectories in Road Networks |
2021 |
VLDB |
4.3363262e-05 |
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VLDB |
4.5008938e-05 |